code: def make_noise_augmentation(model, batch, noise_level=None):
      x_low = batch[model.low_scale_key]
      x_low = x_low.to(memory_format=torch.contiguous_format).float()
      x_aug, noise_level = model.low_scale_model(x_low, noise_level)
      return x_aug, noise_level
  
file_path: StableDiffusion/scripts/streamlit/superresolution.py
imports: import sys
  import torch
  import numpy
  import streamlit
  from PIL import Image
  from omegaconf import OmegaConf
  from einops import repeat, rearrange
  from pytorch_lightning import seed_everything
  from imwatermark import WatermarkEncoder
  from scripts.txt2img import put_watermark
  from ldm.models.diffusion.ddim import DDIMSampler
  from ldm.models.diffusion.ddpm import LatentUpscaleDiffusion, LatentUpscaleFinetuneDiffusion
  from ldm.util import exists, instantiate_from_config
label_desc: Use image node_id 3 for calling a global function with example usage: make_noise_augmentation(model, batch, noise_level) and returns: x_aug,  noise_level